Bibliographic record
Abstract
Introduction: Venous thromboembolism prevention during critical illness is a widely-used quality metric. The purpose of this systematic review was to evaluate the the efficacy and safety of heparin thromboprophylaxis in medical-surgical patients in the intensive care unit (ICU). Hypothesis: Heparin will be effective at preventing DVT and PE in the ICU, as in other settings. LMWH will be more effective than UFH at preventing PE. Methods: We searched EMBASE, MEDLINE, the Cochrane Controlled Trials Register, clinicaltrials.gov and personal files to May 2012. We included randomized trials in adult medical-surgical ICU patients comparing any heparin thromboprophylaxis to another approach, evaluating deep vein thrombosis (DVT), pulmonary embolism (PE), major bleeding or mortality. In triplicate we abstracted trial characteristics, outcomes and risk of bias. Results: Seven trials involved 7226 patients. Any heparin thromboprophylaxis compared to placebo reduced rates of DVT (pooled risk ratio [RR] 0.51; 95% CI, 0.41, 0.63; P<0.0001; I2=77%) and PE (RR 0.52; 95% CI 0.28, 0.97; P=0.04; I2=0%), but not symptomatic DVT (RR 0.86; 95% CI 0.59, 1.25; P=0.43). Major bleeding (RR 0.82; 95% CI 0.56, 1.21; P=0.32; I2=50%) and mortality (RR 0.89; 95% CI 0.78, 1.02; P=0.09, I2=0%) rates were similar. Compared to unfractionated heparin (UFH), low molecular weight heparin (LMWH) reduced rates of PE (RR 0.62; 95% CI 0.39, 1.00; P=0.05; I2=53%) and symptomatic PE (RR 0.58; 95% CI 0.34, 0.97; P=0.04) but not DVT (RR 0.90; 95% CI 0.74, 1.08; P=0.26; I2=0%), symptomatic DVT (RR 0.87; 95% CI 0.60, 1.25; P=0.44; I2=0%), major bleeding (RR 0.97; 95% CI 0.75, 1.26; P=0.83; I2=0%), or mortality (RR 0.93; 95% CI 0.82, 1.04; P=0.20; I2=31%). Some inferences are limited by heterogeneity and imprecision. Conclusions: In medical-surgical ICU patients, heparin thromboprophylaxis significantly decreases DVT and PE risk, and LMWH significantly decreases PE risk compared to UFH. Anticoagulant thromboprophylaxis does not affect bleeding or mortality rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.569 | 0.303 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".